High-Resolution Vision Transformers for Pixel-Level Identification of Structural Components and Damage

by   Kareem Eltouny, et al.

Visual inspection is predominantly used to evaluate the state of civil structures, but recent developments in unmanned aerial vehicles (UAVs) and artificial intelligence have increased the speed, safety, and reliability of the inspection process. In this study, we develop a semantic segmentation network based on vision transformers and Laplacian pyramids scaling networks for efficiently parsing high-resolution visual inspection images. The massive amounts of collected high-resolution images during inspections can slow down the investigation efforts. And while there have been extensive studies dedicated to the use of deep learning models for damage segmentation, processing high-resolution visual data can pose major computational difficulties. Traditionally, images are either uniformly downsampled or partitioned to cope with computational demands. However, the input is at risk of losing local fine details, such as thin cracks, or global contextual information. Inspired by super-resolution architectures, our vision transformer model learns to resize high-resolution images and masks to retain both the valuable local features and the global semantics without sacrificing computational efficiency. The proposed framework has been evaluated through comprehensive experiments on a dataset of bridge inspection report images using multiple metrics for pixel-wise materials detection.


page 3

page 5

page 6

page 7


High-Fidelity Visual Structural Inspections through Transformers and Learnable Resizers

Visual inspection is the predominant technique for evaluating the condit...

RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessment

Due to climate change, we can observe a recent surge of natural disaster...

A hierarchical semantic segmentation framework for computer vision-based bridge damage detection

Computer vision-based damage detection using remote cameras and unmanned...

A Deep Neural Network for Multiclass Bridge Element Parsing in Inspection Image Analysis

Aerial robots such as drones have been leveraged to perform bridge inspe...

PatchDropout: Economizing Vision Transformers Using Patch Dropout

Vision transformers have demonstrated the potential to outperform CNNs i...

A Convolutional Cost-Sensitive Crack Localization Algorithm for Automated and Reliable RC Bridge Inspection

Bridges are an essential part of the transportation infrastructure and n...

A Multitask Deep Learning Model for Parsing Bridge Elements and Segmenting Defect in Bridge Inspection Images

The vast network of bridges in the United States raises a high requireme...

Please sign up or login with your details

Forgot password? Click here to reset